{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 多项式回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.uniform(-3,3,size=100)\n",
    "X = x.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 0.5 * x**2 + x + 2 + np.random.normal(0, 1, size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10645e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/seamonster/MachineLearningClassicAlgorithmEnv/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = lin_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e29ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.plot(x, y_predict, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 明显拟合得不好，解决方案：添加一个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = np.hstack((X, X**2))\n",
    "X2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg2 = LinearRegression()\n",
    "lin_reg2.fit(X2, y)\n",
    "y_predict2 = lin_reg2.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xLiYmRy9VdpMrgp7ZRQm6QnGPceWKyKhRzkUcEGnRglZwyZJ83b8/LXkRkdhYkZkzRcLC+F6FCvRh68JeoQIt8JUrjW0GDhS5dElkwQIRLy9uc/RoxuP95hvedHRBr1CBNxXzsdasmZNXLEdQgq5QKLLG1asi8+fTgs5IyDt1op+7SRO+bthQ5K+/eJzTp0VefFHE39+wlGfNEunZk6+Dg0VmzxY5ckSkY0cRQGIrVpVh//tAyo1bK0ta9uJ27drZTqLaY84cunAqV3b8JOHllaOXLqdQgq5QKFzn6lVaw7eE1eni5kZr/McfDV968eLcPz1dZMcOkV696HZxd+e2P/xAl4enp0jBgpyQvHRJ5NVXRby9Rfz95cDY16X6Sxuk+qjlsrVcAxFAFjfoLGt2nnI+dpNJZMoUjqNSJedjz8j3nkdRgq5QKJxz7ZrIwoUijzySsW8coPAOG0bXx/TptLw9PUXGj+exVq4Uad6c2xYqJDJunEhkJF0eAQG8EQwZQlfM+vX0tesTlhcuSLOpW6Xl0M8lsmiYpGpuMvmhZ6XMrRBBh6SnM6zRlSibhITcu7bZjKuCropzKRT3E7GxzKpcvpzt11JTM97H35/JP6NGAXv2MInn+HHWHZ8yBfj9dyYCnTzJOiwffsimE6tWAW3bspJhly7MHPXxAYYP5xiqVmVtlwceAACEHlyIuaunQoOgf+838VcZpvY7TK9PTWVtdD3JyAntR8zHiGNX81TdlZxAdSxSKPI716+z+07nzkDx4qxbsmcPi2Q5IzgYePtt4OxZ4KmnuHTuzHK3X3zBGuNt2wIvvACUKMFmzP/8wy4/LVsCTz8NlCrFFP7lyynw1aoBW7eyINb+/bfFHJ99hsXLXkGMbyAeGzDrtpgDdtLrk5OZZerl5ZKYD+7+Kv71K4YZmyMzd93uQZSFrlDkR27coBX83XfADz8wpT40lKJ89Srw66/sDGSPsDC2fxs8mPtNmQLMng34+gL9+1NQhw1j8a3u3dn6rUkTYPdu9ujcvp3NKL77ju//+CNQowat+h492MBZ79+Zlsb9Z89GTPMH8ETTEYh2N4prWaTXX7vG0gJvvQUkJHCdp6fTp4yPm/bCtgqsoJjXCmnlBErQFYr8wo0b7Nyji3hyMoVzxAigXTvWIp850xBDa6pVAyZOBPr0Ydeg+fPZii06mhZ4kSK09P38gOeeA0aOZC2VEye4z7JltOo//piVC//7jyVwV65kV6HNm9kRSOfaNdY637IFGDMGJaZNw+SD/9k2kSiUzKeA+fMtb0IhIazT4oCxj4zCyhptjc3zWCGtHMEVR3t2LWpSVKHIZm7cEFmyRKRrV05aAqyHMmoUsyLPnxcZM8b5ZGHjxszkTE/nMX/9VaRuXdvtQkNF3nuPceUiItHRtpEr168zJf/dd7nOx0fkrbeY2GPOsWMiFSty3/nz7X+2HTsY4ujmxu3atHFp8jPd3V1qTVwrZSZsuL1Uefn7XK29kt1ARbkoFPmUGzeYQNOtmyHiISGM9vjjDwrz8eMiQ4c6F7+HHhL5+WcjlO/sWZE+fWy3a9BA5NtvRVJSuN3Nm/YjV0REtm0TqVqV+z32mMipU7bj37yZUTDBwSK//Wb5Xno6by4tWhjRMhMnimzZwhuEo8+iJxK1bCly8+ZdLaSVEyhBVyjyE3FxTJt//HGRAgX4X7dkSVrIv/9uWNcHDzIM0JHwaZpIjx4ie/YYx05IEHnjDdttu3al4OqCn5Ym8uWXRkXELl1EDh/me1FRIn37cn3ZsiIbNth+BpNJ5MMPeROoWdNS7BMSRObOpdUOsELiBx/w5rV7t/Mb06JFTBhq1IhPCPkQJegKxb1OfLzIsmUi3bsbFmjJkqxO+NtvhoiL0D3RqZNj0fP0FHn6abo6dEwmHt962xEjWHbWfLsNG0SqV+f7jRqJ/PIL30tJEZk1S1J8/STJw1Peb95X2kzZZGsRJycbTwyPPcYblAiTi157TSQoiO81bMgxpaby/UWLHH+m55/nOHx8RGrVci2b9B5FCbpCcS8SH8+6Jj16GCJeooTIc89RvMwrBJpMdEXoyTz2loIFRUaPFjl3zvI8O3fabjt1qq0o7tpl+K4rVBD57jvDYv/1V1ragPxSvoG0GvqZfZ91dDTT/gGRl17ijejYMQq87jLq0oWfTz92erpxA7G3vPIKP4O/v0iVKrwx5GNcFXQV5aJQ3G0SEthFZ/lyYONGvi5enGGDPXsCLVoA7mbdckwmhiROmMC4b3sUKQI8/zyXokWN9UePMprFnAULgCeesIxLP3GCES7WkSuensClS8D48cBXXwFhYZj45BtYGlLPooXb7QbKXrGMXb9wAViyhFE3XbsyGsfbm2GUo0cDlc06/yxfDvTubbz28GB4o87IkQx/bNMGCApilEyxYhle5jURUbYRNPks0UgJukJxN0hIAL7/nuK1YQNfFytGgevZk4k57pYtz5CWBixdyqxN6wbLOqVKAWPGAEOGMLxQ59AhoFYty21XrgS6dbPspRkTA7z5JhN3PD2Bl19mTHpAAHtwzplDoU9IACZNAiZPxrI3t9sdSuU9vyD1rVnwLOjD0MkPPmCselAQ8Npr/By6EIvwptapk+VBZs5khmlMDF/36MEbS5s2jIvfupWfOQPWRERh0qpDSExNBwBExSZi0qpDAJCvRF0JukKRWyQmWor4zZsUtIEDKeKtWtmKOMBenQsXMmXeERUr0mJ/8klavgBFcts2oGNHy+SbFSuY8GNOQgIFd9o0ID6eWZ6vv85YbwDYsYMCHBEBPPggLfZbVrVNA2URDN21Ci9tXwAAMCUnwW3WLI5x3jyWBdB7eKak8CY1cKDleGrW5BjHjeMTCQA0awa88QaTlzSNlnnZso6viRkzNkfeFnOd208RStAVCoVLJCYyyWf5croZbt6kC6N/f0PEPRz8N4yLowCOH+/4+HXr0lJ+/HHjZpCSAnz7LZN/4uONbT//nEJtbpGnpwOLFgGvvEK3iF5zRXfLxMTw+F98QXFftozjNjvGuA6Vb1u/Xmmp+GLlFLQ6HXH7/YOlq6LOB2/S9eJ2q9pIbCzw6afAO+8wIcqc3r15g3n9dX6+I0eYvfrJJ8Cjj/K6/PabpZsmAxxliea77FFXHO3ZtahJUcV9QWKiyOrVDOPz8+MkXlAQJwG3bDEiOBwRE8P+l9YTgd7eRru2Nm0Yz21eDjYmRuTtt4364/ry2mtG1yAd68iVxo05yamTns6GykWKsBLj2LEMIXTA6n3npV+vNy3Ou6Fyc3ms/0wJn2AWwnjqFOPl9etivXTqxMlXd3dOoJYrx3j1P/7gOn27xx5z+esQEWk2datFopG+OK3kmIeAinJRKHKRxEQmxDzxhCGoRYsy6eannzIWcRHGcg8ZYitygYFGedsuXYwGEjr//CPy7LNGfLq+PPmkkfBjjrPIFRHGqOvdhFq3tm3sbI7JxLroxYrdPu9FvyLS/H9fWIrmzp2skW7dPzQ8nAlKAHuHFizIqJ7Nm3mT8fBgQ2jr+PiICJe+Fp3V+85LlZe/v2ezR5WgKxQ5TVKSyNq1FE5zEX/mGYqcnlmZEcePsya5tZCXKEFBc3dnSzdzYTWZGObXpYtlQ2Y9ltta9PXz9O7NbYKDRT7+2HKMV6+KDB/O4xUvLrJ4seOGEMnJjBGvVcvi3AP7Tb0tmOHj18nwXq9JdN3GfD8ggMcFmAg0ciRFHGD/UYAZoufPGzcU68915ozr348V93L2qBJ0hSInSEoSWbeOAqtbloULM2ln82bXRVyEWZ01aliKlrs7rVZNo8U9YoRlRmVKCmu31K9vK3jmXYPMcVRzRSc9nfVUgoJoQb/wglGvxZpr11inJSTE8tx164pcvCir952XNlM2yUsPPStngkrzvbAwJgHpbeqaNaPryfqzjxzJxhf2XDErVrh+XfMhStAViuwiKYlCM2CApYgPHszWapkRcRFaz7pFqi9Fi9L9AbB+yUsvifz3n7HPtWvsElT6lkiWLcvtPTyMrkHWae83b9Knbq/mis7+/UZiUrNmfG2PU6dY8Ev3fTdvbvQc7d+fLie9pZye9dmgAa38adMktYCPxHsXlJfbD5deYxZJfKkwy89fvz795dauJjc3Zo5mA8pCV4KuuF9JTuak4cCBFFhdYAYNEvn+e76fGUwm7mdteZYvb9QvKV6c1q+5dXzyJC1m/QbwwANsA1e+PF937kwfujl6zRXdijavuaITG0uL2M2NAjx/vq1lL8I6Kr17czsPD4r3xo0Ua00TmTaNLemGDDGyPjt3pjto/35uB8i2io2kyfAF0mHQbLnsG2jfCteXpk0NF9TChZm7zg5QPnQl6Ir7jeRkkU2b2Pg4MNCwlp96iuszK+IiFMmvv7YVrbp1DVEuW1bkk08se17++SdruJgL6TffiHTowH2qVOHTgTkZRa7o2yxeTP+8ptFnbp3un55Ot1KrVnLb9z1uHMsH7NrFejJ+fizLq9eP8fZmFM/Ro/wckyZx3MWKyct9X5Ey49fL4/2m2xfw9u25vaenSK1akhRY+PZ7f5euIpt+3Jf5626FinJRgq64H0hJoeU8aJCliA8cSEs0KyIuwqiWadNsxatVK9YVB1gHZckSIwImNZV1XHRfc2AgS8cePsx6LB4eHNv779u6eTKKXBHhpKpeU6VhQ1rf5iQminz2mUjlynLb9z1rluHK+eYbI9pGt8aDghgWqddS2b7deOIYNEjkyhUJn7BBprUaYHMtFtTrJAN6viGfth/k0FrfXaqq1Jmw+o4t6XA7Yl5mwgbLkMo8TK4IOoBAACsAHANwFEBTZ9srQVfkCVJSaN0OHmz4gQMC6CPfsMG2GUNmSEzkBKC1OD3yCCNLdF/1+vWG4F6/TpEOD5fbbpiPP+b6zz7jfppGt8bly5bnyyhyRYTx42PHUowLF2Z8ubl7JTqa5XP18dWrx/rn+o0mPZ1uH/PPU6mSyLx5xlPFtWtGNcVy5RiqmZDASVo7Qv1bmdoZul5WVWsjZcetzRZLWlnorgn6IgDP3PrbC0Cgs+2VoCvuGikpjEJ5+mkmy+gi3r8/3Qt3IuIiFM1HH7UUJU9P1i/XQxo7drR0gZw5Q7eFPtHasiUTktLSLLsGtWghss/K7XD5smXkyiuv2E6K6uVx9frlTz9N8daJjKQ/Xo9f79TJsuGFCF0o5p+pSROGaprfEFatohvGzY2umX376J93ItYnCofI+sotbr++XNBW3M1j2e/UklY+9IzFvBCAUwA0V/dRgq7IVVJTGQ/+zDOGiPv7M2587Vrb7MmscOkS/dnmYhQayvK3emZn796WiTC7drEzkLs7lz59mHgjYtk1KDSUTS3MBVaPXPH3dxy5IsLytO3by21/vR6XbjKxlvpjj9Hq9/Li9TlyxHL/fft4gzH/XLt2WW4TFcUblj5We6GUZsvXrXrLoB6vSZ3nl0j4+HWysVIzh9vOa/R4tlvSKsrFuaDXAbALwEIAEQC+AODrbB8l6IocJzWVj/tDhjAUEOAEXr9+zOTMDhEXYWSJl5elEDVowLZwbm58b+hQo1FEWhqtb10kAwJoneuJMnrXIB8fWsyvvkrx1nElckWE9dT1CcZChURmz+a+un++8a0knyJFaNWbh0aaTJz8bdvW8nPNm2d5jrQ0hjA6EW+L5csvLXZvNnWrfNqwm8PtJz866p61pHOK3BD0BgDSADS+9fpDAG/a2W4ogD0A9oSFheXOp1fcX6SmMlFl6FAjBtrPj2n4q1dbRo/cKX/8YStCHTty0c87dqxhNcfH06+tx5iXKUN/ue4eMZkotGG34rJ79hQ5fdo4nyuRK/p2q1cbxxkwgGIdFyfy0UeMpAE4jk8+sbxZJCVRdKtVs/xcQUF0uVy5QqF/5RWO35Fwly7NJyLzddZx7enpcqlRC8fHWLPmnrakc4rcEPQSAE6bvW4JYKOzfZSFrsg2UlNFtm4V+d//jMk8X1+6K1atyl4RF2HUiLX4PPGE0cy4aFGRKVOYPi9CQZ840Zh0bdyYwm1e0+XAASMypXZtRoiYs2uXEZXiKHJFxLJ0QI0aFPwLF2ip6+dv1ozXxbzjUUyMyFtvGen4NWru0DG8AAAgAElEQVQYN0SA8eSVKjm3vsPDRWbM4LGioy3fu3bNOFdyMmPKnR1r7dps+7ryG7k1KfobgMq3/n4dwAxn2ytBV9wRaWnsKj9smFEMyteXPuqVK7NfxE0mipW18AwbZtQwCQ1l4+P4eO4TEcGJVk9Pul66d6dVb050NI/h5sYbwdy5lkLrSuSKCD/va6/RV+/nxxDDiAiGC3p60kfevTtj2s05fpwlBfQWd4ARqmi+BAfLhVbtJdbH3/a9hx/mk4M+7l27jPdKlTImTW/cEJk507jpOlqeey5bvrL8Sm4Jep1b7pSDANYAKOxseyXoikyTlsbIi2efNSzJggVZuW/FCkvXQXaec/hwW9EZPdpIT69ShSF5yckUrw0bDN+zry+jPE6csDxuSgrdH4GBnAx94QXDohdxLXJFZ8MGYyx9+rBQ1sMPG9fnueco3OZs327EwDtaPDwYG793rxweMcHm/QWNu8mPa6xcPvPmGdsMGsR1Fy/yCUHPss1oUThFJRYp7l3S0ig+I0ZYinjPnnQ75ISIi9DKfughGytVxo5lZiXAhJxVqyjiCQkUM926LV2a9VbMXQ06P/1k+KgffNCycqJ15MrQoXSZ2OPUKUaoAIxXHzSI7hqA1+qtt+j+EGHVwmXLjPh280W33vVQSx8furB27eIxrbZ/q81gqTJ6hWXESWqqEZED8EkjMpIT0tYTxlkQc+VLN1CCrri3SEtj/Y/nnjPE08eH4X/LlxsujZzgwgUjDV9f6tShRa5nj7Zrx4lXk4mTja+8Yvib69WjVWvPLXL8uCHA5cox0kb3g6eliXzxhRG58thjtuGDOklJFGvdTRIUZLidqlUTmTOHgjxzJm98hQuLjXB6ebFmy9mzPOann9IiDwujv99O2OG0VgOkzPj1ttmVly4x9lzfdoBtJuidivm9HDee3bgq6Bq3zR0aNGgge/bsybXzKfI4JhPwxx/Ad9+xz+XFi0CBAmwz1qsX8Mgjlo2Osxt7jZMfeQQoU4Y9PJOS2ER54kSgYUPg77+B998HFi9mj87OndmQuWVLy7ZuAFu/vfMOmxzrzZZHj2a/TxE2RJ4wATh8GGjcGJgxg8exx08/sZ3cP//YvlexIlCoEHDggGXfUB03N+Drr9nWTW9Rl5YGvPgiMHu242szeDAeCO+JUzeNPpxupnSEXr+EZyJ/Rv9fvnW8r6s40Z7m726z7FN6i1KBPvhjYts7P/c9hqZpe0WkQUbbqZ6iitzFZAL+/NMQ8QsXKOKPPEIRf/TRnBVxgAL50EOW6556iv01v70lVE8+yV6eVapw+w4dgB9/ZHPjZ54BRo4EKlWy//mWLKFYX7zI3qHvvms0W969m42Pf/kFqFCB16F7d9sbAgCcP0/h/e47x5/l/HmgZEl+nj//BK5d47G6dgXGjmVjZXMuXQJKlHB8vLp1gQULgIAAvLPuV/y5/ReEXzqNyjFnUeHKORRIS7G/X+nSHIurZGBI3jc9QLMZJeiKnMdkYtf45csp4lFRtFTNRdzfP+fHsHAhmySbM3IkcOYM3/PxYWf7MWOA4sUpzD170oouUQJ4+23gf/8Diha1f45du3i8HTto0a9aBTRpwvdOnABeeonXIDgY+PhjYOhQWu/miABHj/K6HD5sew5vb77XtCkt+8uXgQ8/BDZuNMY/ejRvFuacOsXPtXq15bGSky230zSgeXPg5k00BdAUwOWAIJwJKGYr5h4eQNu2vNFlo5gDQEigj10LPSTQx/Xz3I+44pfJrkX50O8j0tMZLjdqlFFLxNub/SC/+cZpw+FsRQ/ts/bdTppkRKUEBtInHh3NZcoUYzK2Vi3GTzur9XLxIkvs6hOT5l2DMopcuX6dE6Zvvmm/DZ0+l/DNN0YdluRkka++cjwZqpOaythuPfolo6VECc4VvPAC/et//MEJ3t9/z5p//A6iWRz50CevPnhfTpTCRR+6stAV2YcIsHMnrdDvvqPV5uUFdOwITJtGn3NAQO6M5coVWqSLFhnr3N2ByZPpv546la6KGTNodUdF0c+9aBF95x07cv+2be27QwBatx99BLz5JvcZP57HDwgAEhKADz6gu+XmTbppXn0VuH6d12bHDuCvv4AjRxxbrF99BfTpY1jxsbHA9Om0yC9cAKpVA774AujXj24rgBb7Tz/RSo+Ozvg6NWvGa1G9uu2Txz//AFWrAv/9l/FxXMFkynCTNRFRmLE5EhdiExFY0BPeHm64npiKkEAfPFAlGCv3RiExlX79qNhETFp1CADQtW6p7BnjPY6aFFXcGSJ0Negifu4cRfzhh+mu6NyZk3a5xcmTwODB9FHrlC8P9O3L8UVG8vX48cCAARTVmTPpsvD25rpRoyiWjhDh9qNHA8eP8zPOnMkJyvR0um9efZWi6+UFdOlCId+5E7hxg8coXJjumMBAw2+vs2ED3VH6jeT0aYr4F19wsrVtW2DQICA0lDeEw4c5wfvrr86vzcSJPEZMDG8C779P9481O3dy2+3bM7jYmcBkcnxjvMWaiChMWnXotmADgI+nO6Y+XhNd65a6rydK1aSoIucQ4eSeLuJnz1K4OnSgn7lLl9wVcYA3lR49eEPRadWKovnNN8BbbwG1awNLl3J8K1bQD71/P0Xt9deB4cOBYsWcn+fYMQr+5s2cMP3hB37utDR+9pdfttw+JYW+9Jo1gSee4HiaNKGodu/OSUqdNWuAxx4zXu/ezRvDDz8Y64oUoYD37+98nB4eHBPACJn0dD4lhYbyCaVjR8vtRXhtnnjC/vHc3FyysO3igpgDwIzNkRZiDgCJqemYsTkSXeuWUhOlLqAEXeEaIsCePYaInzlDV0CHDnQ5dOlCazM3MZloKXfpYrm+Z09GlSxeTKu1ZUvgs884ifjZZ5wwdOSycERsLDBlCkP9fH2BSZOA+vVpxXbvTreKOZ0784bRtCnQoAEjd9LTgbVrOQl84oSx7Zw5FOgjRzieadNo+Vvj68uni+rVgbg44Lff6GLR8fTkZ2/Thm6UCxeAxx/njSIqCnjhBd7YzKOIYmL49LJlS8bX2pyGDRkK+dprtp/dnPR0l8QcyDiyRU2UZowSdIVjRIC9ew0RP32aotG+PfDGG7Qmc1vEAfquv/4aGDLEcv2QIbQklyyha6JTJ7oOiheny6JHD/q227cHvvySN6OMxCY9HZg3j1au+bqpU223bdYMmD+f4Yzmx715k6L93nu8hua0bk0BHzHC/vknT6ZFX6MGrfNvvuF4DhwwtilVivMAQ4bwptu3Lz9neDjdOTVq8ImkcWNuf/06j/Pss84/ux1W1WoPrzmz0enTtxgW6Yz0dH4fLpKRYI/rUNmuS2Zch8ounyPf48rMaXYtKsrlHsBkEtmzR2T8eKPkqocHy8MuWGBZeyS3uXqVNcOtIydGjWJBLC8vps4/8YTIwYOsOti1K9PcPT0ZiXLgQMbnOX+edWL02uHmS0iI0TxZT6G3V3MlMZFt7uyl3ZtnbppnWwKsfbJ4sWXW6YEDLObl52e5bZs2LIWQksLvbfp0jsf8+G++yaiY2FhGxjRsaDuO8eNZWdFJdEqCh7dM7PCc/FamtmvRLPaqQmaAK9mh92s5AKjUf4XLmEwie/eKTJhgFHzy8GC42/z5tl3hc5tTp1gm11o0xo5lxxw3N4ZEDh/OWiLffmsIV5EiIpMnO66NkpjI8Dw9Zb50advzlCrFmijHjtnWXDl9mjePb78VefllNrhwJnTVqvFmsXYte5rqzZY7dWL9Gl0IExMpwE2bWu7v60txP3TI+AxJSWxqbb5dixYiO3bwGJ062f9MP/5o/waZS6GJ9rhfBTsjlKArnGMysc3YpElGHRMPD5EOHdjs4G6LuAhvMtZi5OMj8uKLRhEtf3/eiCIjRd57z2jwUKkSi0WZF/IymXhz+OYbxlo3akTLXT+2HnuuLxMncv+0NB7L/L3q1UWqVmXVRFdEbswYWvHbtxvWsLc3C1mZ12/55x9uq7fM05dKlVimNzbW8hr99x8/h/m2zZoxpt1egaz+/UXeeYeFubJbyO9QzBWOyXeCru7c2YDJxHrZL71kdNBxd6c4fvGFbWLK3Rrjpk1GvXFzQRs1yrBYg4MpTBERLKKlN2Ju04ZNn9PTWdBr+3aRd99l4StzwS5YkM0jJkxgp5+PPjJuBnXqsKvQlCm2bg6Abo0KFXjM8eN50/HwMMbVtatxk2zZkmP89lu2qANYA/3VV1ngSoQuk5UrWYXR/Dxubmw19+OPlk2ZdSIickaUb12ff58cKp837eHa9tWq5erP5H4jXwm6qrx2B5hMbAM2ebJIxYqGiLdvL/L555Zd4O8mycn00Vtbpi1b0pquUYOvy5Rhw4dt2+gicXOjmD7xBEVz0SK6XurWtbSeK1akdfrJJ3wySU5mvfI333RNsCpU4LH37qXVfv063TR6ffEqVejDHjyYgl+sGCsgzpxptG2rWNHyqeHcOQq7tR+9aFHeaE6dcny9PvkkZ4R8+XLD7bNihWv7TJpkMTRlfGU/rgr6PZFYVOeNHxGbaFtJ7n5IKMgSIkw0+e47Rqj88w+jDdq2ZR2Qbt2AoKC7PUoSG8tQwgkTLNd36cKCT5s2MTKkWjUWtSpQgKGDf/5pbFurFrNSr17la39/RnQ0acJ/Q0KY7Xj4MCsmHj7M6B0rLjdphaM30tH6yB8AgLQCPvB4b4ZlzZVz55gd+tlnTBJq3ZoZpVeuMFnpyhUWxgoKApYtY0RJy5bcpnNnHuOnn4C5c4H16y3DARs0YDRN7978nHFxDA89fZr/HjgAfP551q9148ZMGrJHixas8xIUxCii3r0ZYpkR333H6KFbZJQcpMgariYW5XlBXxMRhVHL9tt9TwNw6t1Hs2Fk+QARCtXy5VwiIyniDzxgiLi9rMC7xblzTI2fNctyfZ8+TJ1fu5ZJN02aMKTv118di1m1atwuLIwx1iIscHX4MJf4eGPb4GDLlPhGjYDly7HpxHXETHwVffZuRJq7Oz5v2A1fNe+JV/o2phBFRDAbdNkyHr9nT2zv1B/zd0Xh+VUfoOH5I0grUAAeFSow+chkotCNGcNzREeziuGnnzKb1ZyAAIZYFitmKeD6DSqrPPQQw0sbNGBI48KF9rerXZuhnOfOMcPW1VT/BQtYpdIMV7M59RT/qNhEuGsa0kVQKtAH4zpUVsJvh3yTKTpjc6TD91RCASxF/NgxinibNkxL79Yt48zH3ObAAcZjL15sub5/f4599WojPb5cOdY82bHDcttatZg67+8PFCxI63zlSlrDOsWKMQHnqacYh129OsvZvv46xfXBB3lDKVsW+OADtJ7yNgqkJGFZrYfwfosnEO1XBBDBbx99ha7ntgJbt/Jm8fzzwMiR2HA2EVfGTsKC3evgLrSyPZKSkHbiJDxGjGDVRT8/xsS3amVb1dCcGzcYF16wIGuxFy7MDNPM4ufHWi8dOvBanDvHGPc1a5zvd+AAfzvOxmjNiRP8fqxwJZvT2opPv2VUqtosd06eF3Rnab33bULBkSNGss+RIxTC1q0pIt26MZEmLyFCN8OMGbYZif37Mxnm669t97O2ZIsXp9AdPGisK1KEgv3EExRtfTF/Gjlxgpby2rUUoTVrmK25aBGt2AsX8EfFJpjWaiBOBIXCKy0VPQ/+hGd2r0blmLNM3Jk2DRg6FGtOxmP/hLkYveZDFEq2zZA8GhyOmitWMJHJGcOHM+uzTBkmAAUG0o00a5alO8lVatViMtGLL7JQmCssXcr6M0WLcgyucumSQ0PBlWxOeyn+Ouap/orMk+cF3dEPpHBBz/vrSz961BDxw4eZidi6Nd0Rjz/uvGnB3SI1lS6K6dPp07eHPSG3R6FCFEDd2taX4sUdZ3tadw2aOpV1WLZsAerU4XVs0gRYtgxv/J6C+IuX8exfy/HU3vUodvMarvoEYE2jTug6vDtw4QJiHuiArvt3oauTYdY8e8T+G/36MeuzalVj3ZkzwCuvuH4N7OHuzjkCPz8eu3hxdoFyRrdu/F48PVlnpn591893/bpNxUzzComFfDzh6a4hNd1w5Vpnc2ZUe0XVZsk6eV7QHaX7vta5+l0cVS5x7Jgh4n//TeFq1YrNEbp3z5siDtCF8PnnFNArV7J2jB49WAdFF/CQEJdrgth0DRowgGM5f55VIH/5ha6WmTOBevWALVvwxxtv2BymSOINdN21Adi1AQBgPY38Y8UmWFu1NepcjMQDJ/agwlWrJg9Vq3KSs39/FsrauJHp9ndSxfCpp1iJMTSUS4kSFPUdO1hXJSMxf+EFVllMTKRVf+yY6+dOSGATDTOs3SexianwdNNQuKAnYhNY9tbaL+7ISDN/X5E18vykKGBpAdj7geQrIiMNET90iCLWsiWLLnXvzhreeZVdu+j6MC88lRmmT6cFbd3FJ7Nj0LsGFS1KF8TNm7TUXSDBqwC2lG+E68VKoWbzWqiz5mu6tW6xtXxDTG89EGlu7ugX8T26/73VruslU7RuTZfHzp32BXbMGI7fy8v2vV27KOTmFRkd8cYbfCLYvNm22qIzSpakP17vSWpGVkra2ouE0VERMfbJN1Eu9wX//GOEGB48aLQB69WLIq73o8xLJCXRGlywgNawqxQtSr/xgQO0pLt2peg2b+66BQ7Qwjx71ogK2bmThbEyomZNtmMzj3zp3p3+/bJl+To9na8nTTK2cXPDvPaD0Hz/dtS8lMUbFkCXyKxZdJPp1SKXLqXrw5qHHgK+/95+gavduznBu2mT43OFh/MJKS6OoZ5PPMEoop9+cn28X3/N/qoOKDtxI+wpSEYRaCrKJXPkmyiXfMu//xoirlfOa96ck2ndu3MiLq+QnEyr7osvGDvtCj4+FF2AE2+1a3My7bffeDN49lla0uXL298/Pp5ibR7GZ/6veR1xax5+mFEpqbdyF9q3p6Bt20Z3Q3w8xe7552mpXrtGS/erryiS9jCZMGzzl659dnNKlWJMd8+ejANPTWUPzqef5iSto9Kzep11a/bs4Rj1HqL28PRk9cVffqGbackSrnPUC9URP//MiCknZLWkbde6pZRw5wB3bKFrmuYOYA+AKBHp5Gzb+95CP37cEPH9t2LrmzUzLPHSpe/u+FJSeKPZtYsxyxl1wLHmwQcpGr/9ZtQbDw2lAEdG8knjhReYqOPmZivS5n9b+969vBhnHh5OC79MGc4rLF/O9x95hJbv778b3YIaNqSQLlxo4TYBQN/z5ctZb9pwi0vN2qB4QizHfe0aV4aG8ry6iJtMFMelSxleGRvr+IAVKvCmWaWK5fq9e+kyWb+ekT1xccYNy5yGDSn4L77IMX34IbBuHW8A9vDzs3xa0bFutuEAlUiUO+Say0XTtBcBNAAQoATdDidOGCIeEcF1TZsaIh4amvtjSkvjzUXPnFy71hibI+rVo0vk7FnbXpUdOzKGets2ilr16jxHpFkOQdGiTLC5cIFCYy1qBQoYYh0ebvl3mTIUYN31cPQoo0b0MT/6KN0o1qJtj0qVjAnF69cZ954ZfHwYwpiSwsnNH3/k39YiLsL2dkuX8vu/dIkNKgoUsL1ZhYfzJtauHX8nRYoY70VEUKDXrWN8esuW/NsaDw/62R95hE8o168z+/P77+1/jtKlab3bY8+eTEW+3FdzXHcJVwX9jmqzACgNYCuAtgA2ZLT9fVNt8cQJFoSqV8+od9GkicisWSJnz+beONLSRP79V2TNGnaF79vXeX1ufencmcWioqNZdnbSJJHAQNvtihVzvUaInx/rsTz6qMiIEax7sny5yM6dLFKl1w8xmVgkLCKCRbbmzGHVw379jC73mVn00rcxMTy2ySSyZYtthUJnS/nyLEM7fz4LcelVDMPCWPlxxw7j2Hv3iowbZxT6KlCARbwee8woiKYvgweLtGvHv0eMsKyBvn8/i3wBvPYTJrCgmL3xtWzJapO7d4sEBGT+GunL00+LpKbm3u9T4TLIjeJcAFYAqA+gzX0v6CdPikybJlK/vvEfpHFjFmc6cyZnz52ezvOvX88bSf/+LE6l19p2tvTpwyJdhw7xBqAfb8sWlofNqjj8738iq1ZR4K5cMQTv2jXWD9+4UWTePNYQHzhQpG1bFq/y8bE9lpub7bp+/SzLw7ZsaQh+UJDI66+LXL7Mz5OYyGYX9po7OFoCAjiuyZOdi7iIyOHDbHKhFz/z8OCNa+5c1hvXC3jpS716vMlWq8YCYp98YnyXBw6wxjvAZhdvvCHy3HOOx/nxx/ze3n47698VwMJmijyLq4KeZZeLpmmdADwiIs9qmtYGwFix43LRNG0ogKEAEBYWVv/MmTNZOl+e5PRpw52iu5IaNaI7pUcPugqyExE+JpsXmTp8mK4GZ30dAT6St2/PRJpGjThZFxtr68PeujVrYyteHBg4kO6PxESO89w5y+X8eVt/rZsbfeu6G6R0aeNv3f/+zjucOG7ZkmF369ezuFVaGpNcPDxY96RiRZY8aNyYIZ87d9IlkpkwSv28Xl6M4klNpe++Rw9+r40a0fV08iSTc5YuNSKTHniAUSQtWnAi8pNPDL86QLfU1Kl0SfXqxe9zxQoWTTt0iD7ylSv5mUaP5nflKLywaVM2pf7tN4Yt3glHjlgmPCnyHDnuQ9c0bSqA/gDSABQAEABglYg4jHHKFz70M2cMEd+9m+saNjREPDMp1I4QYUKMLti6gB85YtQ5yYiAAMYPlyjBf7286P8+fZrCqneEd4U2bSj+++0XSQNA37B1yrmm8fzWIm0u3iVLUpCtOXeO1QuXLuW2b7xB//u0aZwQtEZP99+3z/VrZI2/Pz+DIxGPiuJ3/+23nDgGOKndpw+3vXmTyUoLFtCvXqkSQ1JFKMxz5zJaaMQI3nzWreP5pkzhcf39GYf/zDNIrlMP3tccJGU1bszQSlf+L3l58YZp3ctUx07mpyLvkSs+dH1Bfne5nDnDbjjmPSYbNKAf+ORJu7u4XBP60iWRrVvZYOF//2PrsMKFLR+Hg4Lo/ggNte/LzmjRNPqSmzdn3fCXXhL59FO6RJ5/3rYGeWaWunXpH37uObqcliyhe+PUKdYczywJCXQz+PjQ//zyy7w2ISHOx+HhQXeXuVvFzS1zfnd77pToaLpOWrc2+nXWrcvPqtcr37VLpEcPvu/lJTJgAF1Znp787pYsoW/6+ee5f8eOIn/+KdKrF/fx9+fnvHKFvnKzMUWUrJip7+NMoeKysvoDkqrZcVOZLy1bZqnvp+LugNysh+7M5WLOPWWhnz3Lx+HvvjOq/dWvb0Qy2Kk0p2MvlKtk6k1Mr+qOlqmXLd0lMTHGjoUL09IsXZpugkOHXCu05OZGK9ZehEhICN+/dMlwg+zda4T7ZZby5WmFduhAqzy70N0PY8fy2nfrxuvhKFnI3Z3x3Y0bszxsVBRLIvz6K+DtzUiR6OiMn0TsWeLXrzNsb+lSJuGkpwOVKwN9+/KcVapwvJs384lh+3bWmnn2Wf5GXn6ZGZ/9+zOU0t2dx9+yhVEovr78rL6+DON88UVu36LF7WGtqv4AHj/8c6Yu4bZyDeCdnoLmZw463S7OuyC2/hmJrvXucpiswmVy1UJ3dcnzFvrZs4xEMW/MW6+eyNSpIsePu3aMa9fkf//7UCZ2eE7m1+8sv5WpLZd97VjVfn4iJUowgiIoyDULzNubkSpvvMHuOVu3ivz+Oy3iZcv4FDFyJCfVGjViJxzzLvCuWPL21gcFsbPOf//lzHXfv9924tDe4uPDyTu9VV5CAp80Kle2tNTtTaJmZInfvMlr2LWrMQEaHs4Im/37je1SUkS+/lqkZk1uU6oUJ77Pnxd59lmuK1NG5IcfuP2xY8ZkqX6NfX0ZORQTwx6hLjwhxXsWkB2hNWzfCwiQJA9P2VSpmUvf8cwW/aTMradGxb0D8lMLuhzl3Dn2j2xm9h+ibl32q/z3X8f73bhBQfjyS/a0fOgh/ufOxOOx06VLF5EPPqBbZPlyjnHMGD6mN23K7vT2GhT7+rIdWvv2DIt77TWRQYPsR48AdFO0bctemNbvValCwUxIyN5rfvkyo1xGjMj4Onh7szVdUpLl/q+/bn/MjhY/P1sRT0oSWbuWN0lfX25XsiRvin/9ZemSiIvjd6DfeKpXF1m4kG6ldev43Wsa+57GxXGfzZttv5sJE+jGMZn4u8lg3H36vC2jHx0t8Z4F7vg3Nbj7K7dbOIZP2JC936kiR1GC7ozz5ymWzZsbP/g6dRj69c8/ltvGxzO+d+FCxhd37Og4HjirS/nybG7cvTt96GXKGE2HzZcCBWjttW1LP+3kyQz927iRoYBXrxoilJbGWHLzpw3zpXFjnlNvrmy+tGvHY9prTJxZbt7kU8SsWfQrly3r2jVp0oRWrrmoHjvGeYYCmRC3UaMsRTw1lU2XBw825iOKFBEZOlTk55+N0E2dS5fo39bnNVq1Etmwgdfmv/94gwVose/YwX1MJttQw3HjeCM6cYLzGBmM+6NWT8quUtWy7TdWfuwai568ykK/t3BV0O+f4lwXLjAkbPlypocDLB/aqxd94mFh9GMeOsSMx40bbTMiHeHrS99yUBAQHQ3TkSNwS7dfwN8hnp72o0HM1xUtmnEBq8REprrPmsVsUHPc3ekndndnhI55hxpPT/qIR49mrfCskJ7OLM5du4zl4EGuB3iNb9wwskRr12ZJ1n//NY7Rvj0LY9WuzdciDM2bOdN+hqQ9HnqIkSO6T9xkYtMIPWvz8mVGlHTrxgiVBx+0rfB4/DjPuXAhr1O3buxp2qQJx7RwIasg3rzJUgPjxjGi5OhRljzQ6d2b1RQPHOBvzzyMMZcIn7DBZl3hgp54rXN1ldF5j6CqLQIM/TMXcRGGiwUHU4SPH2fKuAvc9CyAP8vUwlWfQkh3c4O7mxse8L6JYkcPWLY+c0BicHH4VKrgON46OEkcfjoAACAASURBVNh+VT1XiYkB5szhxGBMDFPU09IYguflxYlDk4kia16/pHBhYNgw1u3OTFVHEU6ymov3nj1GnHmhQhRUfQkKYnnctWv5ec+dszxe586M29br2aSl8bt77z3XwvMA9uscMoQiLsLJ36VLGS9+/jwncTt3poh37Gi/uNXu3RznypUU+YEDKdyVbzVoOHGCha+2bmVc/GefcZL0xAmGHG4wE097nzMXefmhZ7G47iMO31c1V+4d7l9BP3+eBYneey9r+5crx/+8Pj60ZDUNOyNOIuziKZSMd6FZQ+XKjJqoU8cQbr0JQU5w/Dit8YULaZ0HB1PEY2N506pZkzHR+/ZZ7lehAq3xgQO5XUbExlJYdfHeudNoJuzlxc+ri3fjxjy+m5tl16D0dMNa13n8ccZt67HQcXHAl1+y36erSWjLl/Oaaxojh5Yu5XL8OEW5QweKeJcutMytEWHEyvTpLKKlR6w8/7xRfz4tjZUaX3uNx5w+nTeP06d53MOHXRsrwNj1rLSZywR1JqxBrFkxVQ2wW+bWvG65qsmSd8l/gp6SwgSU1FRaPXp244kTTPRwlDjhiIAAZsf5+LBY0rlzzqvgWXG8SGlsqtICL8yZQNHMKcF2xI4ddE2sXk2BCQmha+DiRVrdlSpRHK0LVrVqxTC5Tp0cjzklhS4Cc+vbvPFC5cqW4l2rFkMFzbHuGmRNjx5sjKy7OqKigI8+4o3Y1QqI48czc/TSJUPE//6bN5K2bSni3bpZFrsyx7pFXqlSvMkNHWop/BERwODBTKzy9qabLitt44YM4RjtJUZlF4GBwNWrWLP/goU4O+oQpNctV1UT8zb5S9ATE5k2nVUKFqSv1pqgIFrRvr4UlKgo227rhQvjr+CK+KtYRewLqYKDJSviRgE/AM67suQIJhMf6WfMoAupUCEKeXw8b0jFijH+/OpVy3R3PQ76xRfpejFHxCiZqy8REcZ1KF6coq0LeMOGFA1nmHcNsqZXL2DxYkPIDxyg9Z4ZgXzgATafOHSIAqln7DZvbmRtOmvPFx/Pp4BZsxjzXq0abw59+/J3EhnJZf9+WuX3Ctu3019vh4w6C2Wl85Ai98hfDS58fJhwk5nHWnO8vCgCzZuzDGxcHAXwwAGWONXLsLq50XXQpImxVKqES/sv4HM71ot541tHZMtjbFISBW/mTApNqVIU5suXOQlXqhQ/1+XLhrgBfAoZOpTJK3qZ3kuXLMV71y7jycTXl8cdOdKwvkuXdr2T0H//UWgXLrR9r2dPiqi/P28iP/wAvPVWxj0wu3fnjXbnTt6Ymjal+6NDBx6nXj1a2L16ZVw75/JlNrr48MPbVvJfYTURU7A0mn44F0Hjx3Obe5HERKeJXo568+q/YUeNmVXD5nuLe8NC15kzh5N32U14OAXh8cfphrFT2yIrwnzHj7FXrrD+x+zZFJrq1flUcfIkb0hlytC9EhVlGZETHs4Jut69WUvEXLx1v7S7O11F5q6TqlWz5jpKTqa1+9JLtu917crJzpIlud0339B94Yzy5enDNpl4g7DO9qxa1cjarFTJ8XFiYngD3LTJaU9REzS42fUw5x7J7h7wTs9EfZ1bHBz3BmpNf9WlbZ39hjNjoStfe+6Tv1wu5kRH07WgYzLRX/jDMXicPoUnT/6Bfn//hIL/RWX9HIGBDLEzX0JDjb9DQuwXlLIiy4+xp05RIOfPpwugRQtathERtILLl6c1feaMZYRNwYLcNiSEk6B//234o8uWtYw6qVfvztxYAC3kZcsorta0b09LuGpVuoDeeitj90WnTrxhFyxIX785ZcvSndKnD29E+lNDSgrdS7qbJDKS/v7ISJ73LnCycAhKxl+FT2rGZRtGdhqD38rWw77Z/TJ9nhqjlqNQ8aLZ4hJx1fhQvva7Q/4VdJ3JkwFfX2wvUwcnP/4SNc8dQ81Lx1Egjb7fxODi8GnVgpZnSAgns06eNGqoHD1q368OUFD8/dme69o1W2Fwc6Obw1r0zZdChVB20qbMNdDds4f+8RUraCl36EB30c8/cxyVKvH1iRNGv057FCliKd4NG1reBLODn3/mxKM1jRrRNdSiBW8qGXW+KVKE/TUHD6Zb5amnLN8fOZI3jLAwPm2YC3dkJG9+mY35zwEW1O+MN9s+g5C4GPw+7+kMtz8yYgJmRhdE/93r0ObU3kyd6+X2w7G4nvH7KRXoky3WsiuWt/K13x3yv6Df4t3uYzB67Wx4p7O/4vmAYOwIq4WrJcMw9KFq9Ct6e/Nf88XLi9buiROWi1VbrqiAYEQXKYESpYuhRKkgQ+ivXuWE2tmzdH9Y93f098fJgkVxxrcoLgQEIyqgGC4EBONCQDDSS4di5Vs9sebvy3jv+6OoFPE7ntu7BvVOHaC7p0sXHm/DBiauFCvm3LfbtKmlgJcv77rfO7P8/jvjr62pWJG1vtu1AyZOZEy4PfQJ6nr1GP9erBgjdRYtstyuXj2K/R9/OL953QHby9Z3TUz12Pjz53kjjzKe/m54++KBIZ/iegE/fPvtS2gYlUEbvMqV+X398ovLORA6F/2KosvADxDtV9jhNjltLZeduDFzRooiW7hvBL3KmFUwaW74bsl41P7v34x3yCmCgpjJefMmxdc6Wia7eecdZkTWrMmbU04iwkYRDz9s+16xYvTXFy3KhBt7VKhgZK3WqMF///47Z8Zqj/LleYMICgICA3F+3xGU3rzW+T6jR9N/7+fHp4elS2026df7LewIq4mRv3+DF/5aZv84jzzCa6fPA7i5Zakx9e6pczAgviwS0zLeNyetZWWh3x3uG0E3/4EVj4tBvahjqHfhGJpc+gc1/ztuCKuHB8Wkbl3+565Ykf/JkpJslk9/PILkuJvwTk+FV3oqvNNSUDA1CdWvnUfFi5nofpOThIZSKAMCbJ8+7D2RuLqY75uWRh/5gAH2x1CgAMXevIRATlCmDCOO9FK5lSrxCWTGDMcJZF5exk02Nta1jM0nn2Qaf8WKfB0VxUld69/syJFY1/cFxI2fhH6/OihD3KAB3XrmnaSKFWO0z7JllmWTndGpE+dSgoNvu0QcxZTr5KS1rHzod4d8Lejmvr5CPp64mZKG1HTjc9z+gVUtyonEP/9keOKffxpJLr6+dE80bcrMvSZN+J8fmXysTEujq8a8u5Duo88Dvt17gqJF6XOvVs1YKle2zWA1mYBVqyiKLmCChrPBoXBvUA+hbZvTp//tt7YbLlzIm5a5m2r3bv4+zLju44+9C1ai7czJluGhACNutm61L9QPP8ykotBQm2M6pGtXuq3szH04+n3q5LS1rKJccp98K+j2LARPNw1+BTwQm5Dq/AcmQp+3ucDv328I7y3/5rvXi2Bb4XL4NygMohn1VTL1H+XaNT6mv/BC5tq9AZwQHTaME4K6tQgYvTp13729xZWGGPcK7u5ZuikmeXhhX0gVHChZCZ7pqXj8yM8okmCnLd3cuXxas35KWbaM35sZS+o8jH77f7A9xsyZ3GfECNv39OYVZcrw/U8+ce0DZBBT7sjtAShrOb+SbwU92314N2/ykdpc5K+wZssNb1/sL1kJe0tVxd9h1dBt2OPo1MpOM93UVPqE9RonO3faptwD9OW62rC4cmXGnetLpUqcJIyNZehmdDR99frf0dFMGjp69K5U9LOhRAmKWbduFObJkw3reNAgWsSpqbYur8uXWRhLT/ZyldBQwGRC7LU4BCTE5U5cecmS9ssaAKy+6OvL7+uDD1w73scf278xWGHPqAGyp4Kisr7zJvlW0J09bn7Qu86d//hEgOPHsffbjTi3cSuqnPoblWLOwE2Ej+TVqxvFtvRu8xERji3jF19kDPXcuRT5woUZ6RETY+F73luuDoKrVUDY0YjMdakHeMyQEBbmKlaM/1r/rb9OSWF7tXnzmDqfndSvzxDDXr0osCJ0kYwaxSeLp55iyzZzN8KlS3QtTJ9u6W+2xt2dY27Vyij8ZU56OrB9O1aMmoqH//kTfimWN/1kd094r1/L8Ec78ybYt89p8lGO8847nIR1kZwQXuUfz7vkW0HPlsdNk4n9HVNTLQXP19c23C8mhr7R2bMzTlM3p1UrTsRlMjQNbm6czNPFOCCANw3dGreXLOPhQQtet+bLleO4//2XPTGt66LnFPXqGXH4yclG6GJwMK3u8HDe/JYvZ+GujAgIoHXbr5/jSJ7Dh1kWYckSm5BTnb593kZqaBms6FeDKf9xcazLHhfHa2ovwzU3+f13lqW4y6gIlrxLvhV0R4+bOi79+GJiGE+c06GFzujbl/Hc1hZ14cIZ10UXYcOORYsY5ZHbLpbgYGaCNmvGAmHmPvxjxzJ/E6talVb3H3/whtWwITB8OK9PfLwhvroQHz/OOuQu1KHPcR57jMXAtmwB1q/PXHZqfLxrpYtzCRVjnnfJX8W5zNCt71HL9tt936ViQkFBLLd7/Dit6CFDHGeNZid+fqw02KGD00kvALzZnDlDn/iPP7KYVWZdMXqjh2xiYeNuCH/uabTp94hl6v3hw5x7+OqrrCUBHT3KRWf3btsokkyyplEnXNQKwK1QAJrWLoNa1cKYFHbhAicyHdVar12bTxoLFlis/rlcfTxw0iwJyd+fzTK2bWPTjsxw8iTdcHkMR2V2QwLtNAJR5EnuOQtdJ1sfDx9+mP/RzS3lwEBGp6Sk0H2QnMwiT1F3UCPGEYUK0X+c2WiYNm3Yeefhh+lqMS+stWkT8Gj2WFVHg8NxuHh5BN2MRaOoIyiYcgeZm/7+2VMPPDzctgZ+kSJ06YSFWa6/coURR/PmOU5oql2bTxxz51qunz2bk9mPOO784zKrVjELOLdr57uI8qHnXfKty0Un1358qan0+b73HkMcS5RgOFr79vzPP39+9p3LGf7+9LMWL+44cejGDeD113NnPM4oU4aVKzt35mStXi7hjz94Hbdto398+HCGZupdgXSOHqVf/JNPLN0qTZrQJfX9947PrRdWK1GC4n3hgtOhRgcUBSpVQvCevyzf+Pln1hZv3pzRT3fCoUNGhmweR0W55E1cFfQMu0hn51K/fv2sNr22y+p956XZ1K0SfquL+ep957P1+PLllyKhoeycrmkiffuKzJ0r0qBBtnVjz3NLcHDW9itUSOTTT0VOnRJJTzeuYWqqyJIlIrVrc7uQEJEZM0SuX7e81pcuiXz4oXFt3dxEOnYU+eYbkZs3RWJjRV5/necxP2+7diLr1oksXSoybZpI48Z39vmXLRNZvDh7ruXZs9n7e1TctwDYIy5obJbFOStLdgt6jmIyiZQrd/cF9i4tCUHFJTI4XI4GlZFYb1/n25csafnaz0+kWjXbdfPniyQnG9c4IYEC+uijIu7u3K5uXZFZs0QuXuQ216+LTJkiEhhoebzwcJHNm7nNuXMiU6eKVK3K97y9RTp1shX/3Fq2bMn936siX+OqoGfZ5aJpWiiArwAUByAAPhORD53tkytNorOTtDQmhvTpw9DFnDyV5oZ474KI9yqIOO+CSPLxRd3qYQzXO348692aXMXT07ZiZEZ07MhooeBg7h8dzXH++qv97f39GdFy7RpDKnVCQlhHpX9/wzVx4wZ7jM6axe31CV43N8a1T5jAxs5ffcXvRoTlAwYM4HzH8887HPZNzwLwdaFWeaapWpVdsPT2egpFNpHjPnRN00oCKCki+zRN8wewF0BXEXFYPzQvCrpLPsPERNbstlcHRCc4mLHglStz0f8uX57vx8XRH3zjhsUyedEf8E9OgF9KAvySE+B/61+/lAQ097iZ+ciW7CQsjBOrx47Rp6zTuDGTo/QM1ZiYO69bU7o0J3krVeK59PM1asRJzRMnKPb9+zNBa8UKTiSXLUsR79+fE9jVqt3ZOHSefZb117dsyXjbatWAvXszjlxSKLJIrk+Kapq2FsDHIvKTo23ymqBnemI1Lo5t8NatMybKSpRgDXAfH6MBw6VLxj5ubhQdc5HX/w4JQfikTRanCEiKR/t/d+KRyN/R7ux++1ZzWBhvFOYiezdwFBbZvz+jUKKiGP6XTb8xC9q1A8aOZQhofDwwcCDrqmcHzZu7lkTWrh0jV+y0LFQospNcFXRN08IB/AqghojcsHpvKIChABAWFlb/jKP437uAK6GPDi34ffsY5fLFF8aODRtSZFNT6VLQu+roQv/PP5Zx2r6+OOpfEpd8CqH81fMIvW7cCG4U8ENAUnzWP1yzZiwVrFebtIebG+t+P/oox6U/QVy5ArzySpbqdmdI5cpAmTK4fCMJ5y9dR8G4WFSJyd3fRLqnF/6o2AA+N2LR8HwGDSns0a4dM18LFcr+wSkUdsg1Qdc0zQ/ALwDeFpFVzrbNqoWeU6FUGWXGuVQE6fRp+0ki8+YBzzxjGXNsMtFqjYxkIa8ZM+ijzyYSSpbG2vJN8I93YTS6ehptzh+ET/Stm0Tx4rQkfX0Z9nj0KMUboLtILyOclsZQzJMnGXv90UeG2ygtjSGc06fTV1y4MLdp3pwVCn/5Jds+S55k61bggQeyrRuUChFUuEquCLqmaZ4ANgDYLCKzMto+K4LuSFQDfTzxepc7qyyXkYWeqboxZ87QzWCNpyf9sC1aADduYO9HC5H0zVI0/HcvvEyZ7/LuiBT/QjhTIBAVo2ntXvUJwI6ydVCyR2fUfbqXbbJNejp90XqFydWrDYHXef55inWtWqwJM2sWP2fVqqwm2Lgxhfzrr5nuX7Ag488HDGC/UXd33gQuXWKdlXPn+JQyeXK2fe4cp29fNrguVy5bD6uSeBSZIcdT/zVN0wB8CeCoK2KeVWZsjrRbtyU2MRWTVrFaYFb/A4zrUNnuf6pxHSoDcF5GIDE1HTM2RxrnLlOGvuL0dGYXjh7N9ampTFC5hXnL5ARPbxRMter2M3w4XSXff09fvYuTjV5x11Exjkk4kUFh2FilJY4XDcXN2EJYFBRku4O7O9vXVa3KqBDd/1yrFq3QAwf4OWbPttyvVi1mVX78MTB4METTsLNsHSx79EVsrtQUXoUC8HpQNXTVn0w8PBgJU7gw/dIffnh7jKcLh6Dq5VMIM3M1oUgR4OWX6dJavNilz55jpKXlWFanvd+1zW9KocgkdxLl0gLAbwAOAdCdrS+JyCZH+2TFQs/p7izOHnvrTvkR1xIch/JlWLToxAlaqmfPOh3D5YAgFBs2mNb8smWuV0esUYM+6bAwTNt3FSE3olH2ahTKXY1CSJxV55ywMNtJ2atXgXffBQ4eZObrxx9z/cmTrHfy2WcZliNYX601NlRqjn2lqiDarwgANhyZ0bM2r2NiIt1P06bdnixO8Pp/e/ceI1V9BXD8e1gWWARdH4vgwMJqNuCjUJSYKKbF2CI2FrbWt4mlbYKksZWagq+kio0Rg2lqGk2q1UYSVKpYAtGibQUfNVRUHroLNhSqsBJFAS2wCruc/vGbgWV3HnfuzH3u+SQbmXF35tw7u+f+7vn97rmDGHjoIDV6tEb/ZuN43hs3iZt2rSu/F3o1rVnjzjwCNub2Fwr+v/9aIyzTQ+AjdFV9A5fTAlWoYVCOp2ZcRbRMzBQcEZU61pVsWnTGGa5EoeqWPN5wQ95vG/blZ64u7cXMmS75XnKJq4tnLe9RHqo7+BVNez5m0tefcu+42qMTs4sW5e+lMnSoa2nw0ktHnzvzTPceGza4teCnnuoOUM3NsH8/G59bydQP3uT7ba52vv2EU3knM453TxvHyt1baGk+xFf3P8Cgz3cd81aDD/ZeA37hRxu58KON3vZBKSNHFmylm9edd7qySpVq417UiNCV5xesJsQYTPrEvttivrJId0F2gvuio/DovHtppiQRuP56Jn80nPY9B/jZmmeZ99qismL5fMiJvN44gX1bOxi/fgvjBw1ypYxMBoYP77WfOgYMYtvIZmZdcQXkDlhdXfDYY66sA67vyTXXuKWFz+eZz+7eBXHKFHc3nbPOcgeqgQOZ0f9iajsPcfYn/+Hcjzdz3o42WtpepaXtVcgu3w5sZXa/fr1X4Ywe7Q6gHpL51mtmcvqjD0W25DBfMi/2vDFexD6h50bP81e09ip/lJVUfSh0dlAj4mvyKpd0H7ngah654GpED/PTDS9y0+73aFhbfN3zyfv20NK22j3IcyFmS/YL4LPB9ewYNor6iecw5oRzYcdYNyqfP9+N1KdMcbXs1lZ3ZlCgL/zBfv3ZMmw0px/6kkGrV8Pq1cf8/21lbHtFLrvMlYDGjnUHxwcfhHnzen9fiSWxD114Hcum3cgtl0c/8Zgp8LuVsVa1pgKJ6rYY9jKvaq5EyMXevrfjyOl2pvs2dHa6vtpXXlntzSjbB6c0crCmlpMPfMHJB75gYFeZLQGqZcUKd+HQ9u1uXuHJJ+Gpp7z/fCYDzz7rlmTGjK1yMeVIffvcsFTjIOL7j3fbNhb8+F4u3/w653wSYQuAJMhk3ARvTY27i9DgwZ5+LMq14LYO3XhlCT1GKrkZR+5nm3a3c177Jobt203D/j2M+XovFx/f5coMQdx0I+6mTnX90nMXPflgo2STFKm9BV01hD0yKrQSx8sKnVzdfdtJGbad5GLMJZ0jk52q7rL9nTvd7ermzKla7LEi4i5+GjKkKi9na8FN2vS5hN5zVNa+t6PiC5RKqeRejbmYih6ARNyKlfp6t2Svq8td4j97dtW2IUwd/QfSNqyJjSOa+dNF13Lrj74dyGdTyYHWmDjqcwk9ilFZqStSofhZQ7G18r0MHQq33upe7/5/FD8L2bnT3Yz57bfdf1eurHhbc26b9nOWjJ/KsP17eOvhG/N/04gR0NICs2bBhAlMfmBV3gNfUJ+N3RTZpE2fS+hRjMpKjbKrfdawbF07c5/bwKEuPfJ6c5/b0Pv1RoxwNy2ePt097uiAxYvdFaSNje6eoE8/7RJ/U9PRr+HD3TpwcKtzFi50F+cAG4Y3888xE1gyfiqIUDsy4/q81NS4C5MGDCgYd6HPoNiFZZXwcqA1Jkn6XEKPalRWbJRd7bOG+StajyTznENdyvwVrcVfr67OdYjsrufjnvr3hzvuYNm0GwsnxzFH37PYmUihz0ayP1csdj/zIp7KWcYkSJ9L6HEclVV7ZFqo/0yxvjSV8pIcS52JzL10LL9csr5X7x6leNml2OuWiqmscpYxMdfnEnocR2WVjEzjpFRyLHUm0jIxw5wl6/P+bLGSWKHXvWd5K193Hg51AtyYKPW5hA7xG5X5HZkWUl9Xy948fWjq6wrfvDiMpZxe5i8KXRJfrCRW6HXz7YNSpayg9oNdRGTC0C/qAIw7wBS6vMvPZO0908+mtt+xXftq+wn3TD877/fnShbteztQjo5kl62r7gVLhZJy9+fnXjqWutpje5CXKomVO/9RaJ8GtR/C2r/GWEKPiUJNmfxM1rZMzLDwqglk6uuQ7Gsf6U+eR7FSSDV5SdYtEzPcf8U3jom91JWbhV73xMH5z0gK7dOg9kNY+9eYPllyiaNqT9aWU1YKaymn1/mLcktihV4XKGufBrUf7AImExZL6DER5WRtmEs5/cxfeKk/l1oW6mWfltoPfuvgdgGTCYs156pAWia64tykKszYir0X5B/te4kjzvvXJIPX5lxWQ/cpTRNdfurWYQmz/lxsP1QSR5z3r0kXK7n4lLZOfXFbypkTdv250H6oNI647l+TLjZC98kmusLhZaljX4rDmGIsoftkf+DhuHhcA9LjuSBbNSxb187kBa/QdPsLTF7wypESmp/18caEzRK6T/YHHrxl69pZ+k77MRddCfDD84IpXxSbF7E6uEkCq6H7FMeeMGmTb55CgVWbd4X2fj17zdjna+KsooQuItOAh4Aa4I+quqAqUSWE/YEHK+x5CpsXMUnnO6GLSA3wMPBdYAewVkSWq2pbtYIz6VLuuv2wL8ixC4BM0lVSQz8f2KKqW1X1IPAMMKM6YZm08bNuP+x5CpsXMUlXSULPANu7Pd6Rfc6YXvxcmBP2RKRNfJqkC3xSVERmAbMAGhsbg347E1N+69Nhz1PYvIhJskpG6O3AqG6PR2afO4aqPqqqk1R1UkNDQwVvZ5LM1u0bE7xKEvpaoFlEmkRkAHAtsLw6YZm0sfq0McHzXXJR1U4RuRl4Cbds8QlVba1aZCZVbN2+McGz9rnGeJCWVskmmby2z7UrRY0poWc/89ySS8CSuokV6+ViTAl2T1CTFJbQjSnBWgKYpLCEbkwJtuTSJIUldGNKsCWXJilsUtSYEmzJpUkKS+jGeGAtAUwSWMnFGGNSwhK6McakhCV0Y4xJCUvoxhiTEpbQjTEmJUJtziUiu4APy/yxU4DPAggnCrYt8WTbEk+2LUeNVtWSN5QINaH7ISJve+kylgS2LfFk2xJPti3ls5KLMcakhCV0Y4xJiSQk9EejDqCKbFviybYlnmxbyhT7GroxxhhvkjBCN8YY40HsE7qI/EZENorIehF5WUROizomv0RkoYhszm7PX0SkPuqY/BKRq0SkVUQOi0giVyKIyDQR+UBEtojI7VHHUwkReUJEPhWR96OOpVIiMkpEVolIW/Z37JaoY/JLRAaJyFsisiG7LfMDfb+4l1xE5HhV/TL7718AZ6nq7IjD8kVEpgKvqGqniDwAoKq3RRyWLyJyJnAY+APwK1VN1N2/RaQG+DfwXWAHsBa4TlXbIg3MJxH5FrAPWKSq50QdTyVEZAQwQlXfFZGhwDtASxI/GxER4DhV3ScitcAbwC2quiaI94v9CD2XzLOOA+J9BCpCVV9W1c7swzXAyCjjqYSqblLVJN9U83xgi6puVdWDwDPAjIhj8k1VXwN2Rx1HNajqTlV9N/vv/wGbgET2LlZnX/ZhbfYrsBwW+4QOICL3ich24Abg11HHUyU/Af4adRB9WAbY3u3xDhKaNNJMRMYAE4F/RRuJfyJSIyLrgU+Bv6lqYNsSi4QuIn8Xkffz0dEu9wAAAXlJREFUfM0AUNW7VHUUsBi4Odpoiyu1LdnvuQvoxG1PbHnZFmOCIiJDgKXAnB5n6omiql2q+k3cGfn5IhJYSSwWdyxS1e94/NbFwIvA3QGGU5FS2yIiM4HLgUs05hMYZXwuSdQOjOr2eGT2ORMD2XrzUmCxqj4fdTzVoKp7RWQVMA0IZPI6FiP0YkSkudvDGcDmqGKplIhMA+YB01X1QNTx9HFrgWYRaRKRAcC1wPKIYzIcmUh8HNikqr+NOp5KiEhDbjWbiNThJuEDy2FJWOWyFBiLW1HxITBbVRM5khKRLcBA4PPsU2sSvGLnB8DvgQZgL7BeVS+NNqryiMj3gN8BNcATqnpfxCH5JiJPA1NwXf0+Ae5W1ccjDconEbkIeB14D/d3D3Cnqr4YXVT+iMh44Enc71g/4M+qem9g7xf3hG6MMcab2JdcjDHGeGMJ3RhjUsISujHGpIQldGOMSQlL6MYYkxKW0I0xJiUsoRtjTEpYQjfGmJT4P1vXByedWyIeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f42da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.plot(x, y_predict2, color='r')\n",
    "plt.show()\n",
    "# 因为x是乱序的，所以y也是乱的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f5e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 重新画\n",
    "plt.scatter(x,y)\n",
    "plt.plot(np.sort(x), y_predict2[np.argsort(x)], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.11169309,  0.55911883])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.3359107436326534, 1.7425393979250752)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.intercept_, lin_reg2.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
